Version: 1.0.3
Title: Distance Metrics for Trees Generated by Congreve and Lamsdell
Description: Includes the 100 datasets simulated by Congreve and Lamsdell (2016) <doi:10.1111/pala.12236>, and analyses of the partition and quartet distance of reconstructed trees from the generative tree, as analysed by Smith (2019) <doi:10.1098/rsbl.2018.0632>.
URL: https://github.com/ms609/CongreveLamsdell2016
BugReports: https://github.com/ms609/CongreveLamsdell2016/issues
Copyright: Data from Congreve & Lamsdell (2016) released under a CC0 license <doi:10.5061/dryad.7dq0j/1>.
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Encoding: UTF-8
Language: en-GB
Depends: R (≥ 3.4.0)
Imports: Ternary
Suggests: ape, bookdown, knitr, phangorn, Quartet, rmarkdown, TreeSearch (> 0.2.0), usethis
LazyData: true
ByteCompile: true
VignetteBuilder: knitr
RoxygenNote: 7.3.1
NeedsCompilation: no
Packaged: 2024-02-23 09:05:40 UTC; pjjg18
Author: Martin R. Smith ORCID iD [aut, cre, cph], Curtis R. Congreve [cph, dtc], James C. Lamsdell [cph, dtc]
Maintainer: Martin R. Smith <martin.smith@durham.ac.uk>
Repository: CRAN
Date/Publication: 2024-02-23 09:40:15 UTC

Consistency indices

Description

Consistency indices of Congreve & Lamsdell datasets.

Usage

clCI

Format

An object of class numeric of length 100.

Default colours for analyses.

Description

Default colours for analyses.

Usage

clColours

Format

An object of class character of length 8.

Initialize ternary plots for quartet plotting

Description

Sets up a blank ternary plot ready for analytical results to be added.

Usage

clInitializeTernaryQuarts(
  zoom = 1,
  padding = 0.1,
  gridLines = 10,
  fontSize = 1,
  gridCol = "#DBDBDB",
  backgroundCol = "#FDFDFE",
  xLim = c(0, 1/zoom) - 0.01,
  yLim = c(0.5 - (1/zoom), 0.5),
  isometric = TRUE
)

clInitializeTernarySplits(
  fontSize = 1,
  xLim = NULL,
  yLim = NULL,
  gridCol = "#DBDBDB",
  backgroundCol = "#FDFDFE",
  padding = 0.1,
  isometric = TRUE
)

Arguments

zoom

Level of magnification (times), used to adjust ticks and scale.

padding

Padding, passed to TernaryPlot.

gridLines

Number of grid lines, passed to TernaryPlot as grid.lines.

fontSize

Font size, passed to TernaryPlot as lab.cex.

gridCol

Colour, passed to TernaryPlot as grid.col.

backgroundCol

Background colour, passed to TernaryPlot as col.

xLim, yLim

x and y limits, passed to TernaryPlot as xlim, ylim.

isometric

Logical specifying whether plot should be isometric, passed to TernaryPlot as isometric.

Functions

100 simulated data matrices

Description

Contains the 100 simulated matrices generated by Congreve & Lamsdell (2016) using a heterogeneous Markov-k model, generated from the clReferenceTree topology, with all branches sharing an equal length.

Usage

clPhyDat

clMatrices

Format

An object of class list of length 100.

Source

doi:10.5061/dryad.7dq0j

References

Plot results

Description

Plots the results of the analyses of the Congreve & Lamsdell (2016) datasets.

Usage

clPlotQuartets(
  dataset,
  tree,
  cex = 1.1,
  pch = 2,
  col = CongreveLamsdell2016::clColours,
  ...
)

clPlotAverageQuartets(
  dataset,
  cex = 1.1,
  pch = 2,
  col = CongreveLamsdell2016::clColours,
  ...
)

clPlotTheseAverageQuartets(dataset, cex = 1.1, pch = 2, col = "black", ...)

clPlotSplits(
  dataset,
  tree,
  cex = 1.1,
  pch = 2,
  col = CongreveLamsdell2016::clColours,
  ...
)

clPlotTheseAverageSplits(dataset, cex = 1.1, pch = 2, col = "black", ...)

clPlotTheseBestAverageSplits(dataset, cex = 1.1, pch = 2, col = "black", ...)

clPlotAverageSplits(
  dataset,
  cex = 1.1,
  pch = 2,
  col = CongreveLamsdell2016::clColours,
  ...
)

clPlotBestAverageSplits(
  dataset,
  cex = 1.1,
  pch = 2,
  col = CongreveLamsdell2016::clColours,
  ...
)

Arguments

dataset

Dataset to plot, for example ⁠\link[=clResults]{clBootGcQuartets}⁠.

tree

Integer specifying which tree to plot.

cex, pch, ...

Graphical parameters to pass to JoinTheDots.

col

Named vector specifying colours to use to plot each analysis, named to match names(dataset).

Value

Returns invisible.

Functions

Author(s)

Martin R. Smith

Tree topology for matrix simulation

Description

The tree topology used to generate the matrices in clMatrices Congreve & Lamsdell (2016).

Usage

clReferenceTree

Format

A single phylogenetic tree saved as an object of class phylo.

Source

Congreve & Lamsdell (2016).

References

Examples

  data(clReferenceTree)
  if (requireNamespace('ape', quietly = TRUE)) plot(clReferenceTree)

Congreve and Lamsdell tree distances

Description

Distance of CL trees from generative tree.

Usage

clBremQuartets

clBremPartitions

clMkvPartitions

clMkvQuartets

clBootFreqPartitions

clBootFreqQuartets

clJackFreqPartitions

clJackFreqQuartets

clBootGcPartitions

clBootGcQuartets

clJackGcPartitions

clJackGcQuartets

Format

An object of class list of length 7.

An object of class list of length 7.

An object of class array of dimension 21 x 8 x 100.

An object of class array of dimension 21 x 7 x 100.

An object of class list of length 7.

An object of class list of length 7.

An object of class list of length 7.

An object of class list of length 7.

An object of class list of length 7.

An object of class list of length 7.

An object of class list of length 7.

An object of class list of length 7.

Details

For each of the 100 matrices generated by Congreve & Lamsdell (2016), I conducted phylogenetic analysis under different methods:

Mkv:

using the Markov K model in MrBayes;

eq:

using equal weights in TNT;

k1, k2, k3, k5, kX:

using implied weights in TNT, with the concavity constant (k) set to 1, 2, 3, 5, or 10;

kC:

by taking the strict consensus of all trees recovered by implied weights parsimony analysis under the k values 2, 3, 5 and 10 (but not 1).

For each analysis, I recorded the strict consensus of all optimal trees, and also the consensus of trees that were suboptimal by a specified degree.

I then calculated, of the total number of quartets or partitions that were resolved in the reference tree, how many were the same or different in the tree that resulted from the phylogenetic analysis, and how many were not resolved in this tree (r2).

The data object contains a list whose elements are named after the methods, as listed above.

Each list entry is a three-dimensional array, whose dimensions are:

  1. The suboptimality of the tree. Different measures of node support are employed: 

         * `Mkv`: Posterior probabilities, at 2.5\% intervals (50\%, 52.5\%, ...
      97.5\%, 100\%).

     * `Brem`: Bremer supports: the consensus of all trees that are
       (equal weights) 0, 1, .... 19, 20 steps less optimal than the optimal
       tree (implied weights: the consensus of all trees that are 0.73^(19:0)
       less optimal than the optimal tree).

     * `Boot`: Bootstrap supports (symmetric resampling, _p_ = 0.33).

     * `Jack`: Jackknife supports (_p_ = 0.36).

       `Boot` and `Jack` results are reported both as the `freq`uency of splits
       among replicates, and using the `gc` (Groups Present / Contradicted)
       measure (Goloboff _et al_. 2003); frequency columns correspond to
       100\%, 97.5\%, 95\% ... 0\% support; gc columns correspond to 100\%, 95\%,
       ... 0\% present, 5\%, 10\%, ... 100\% contradicted.

  2. Counts of the condition of each quartet or partition: 

      * `Q`: The total number of quartets defined on 22 taxa.

  * `N`: The total number of partitions present, counting each tree separately.

  * `P1`: The number of partitions in tree 1 (the reconstructed tree).

  * `P2`: The number of partitions in tree 2 (the generative tree).

  * `s`: The number of quartets or partitions resolved identically in
         each tree.
  * `d`: The number of quartets resolved differently in each tree.

  * `d1`: The number of partitions resolved in tree 1, but contradicted by
          tree 2.

  * `d2`: The number of partitions resolved in tree 2, but contradicted by
          tree 1.

  * `r1`: The number of partitions or quartets resolved in tree 1 that are
          neither present in nor contradicted by tree 2.

  * `r2`: The number of partitions or quartets resolved in tree 2 that are
          neither present in nor contradicted by tree 1.

  * `u`: The number of quartets that are not resolved in either tree.

  3. The number of the matrix, from 1 to 100.

Source

Congreve, C. R. & Lamsdell, J. C. (2016). Implied weighting and its utility in palaeontological datasets: a study using modelled phylogenetic matrices. Palaeontology 59(3), 447–465. doi:10.1111/pala.12236.

References

Goloboff, P. A., J. S. Farris, M. Källersjö, B. Oxelman, M. J. Ramírez, and C. A. Szumik. 2003. Improvements to resampling measures of group support. Cladistics 19, 324–332. doi:10.1016/S0748-3007(03)00060-4.

See Also

clMatrices, clReferenceTree.